M1 - Intro to Data Analytics

Learning Objectives

• Describe the typical data science workflow
• Understand what it means to “tidy” data
• Differentiate “primary” and “secondary” data
• Differentiate “qualitative” and “quantitative” data
• Identify different file types used in data analytics and discuss why some formats are better than others in terms of transparency and reproducibility
• Describe the various data structures used in data analytics and what each are used for:
  Vectors, matrices, arrays, data frames, lists
• Understand the difference between R and RStudio
• Become familiar with the typical layout of an RStudio session

Recordings

• Intro to Data Analytics & R

Assignment

• Assignment 1: Using Git & GitHub

Resources

• Frank E. Harrell Jr’s R Workflow guide: http://hbiostat.org/rflow/